Automated Speech Analysis for Risk Detection of Depression, Anxiety, Insomnia, and Fatigue: Algorithm Development and Validation Study
This study demonstrates the potential of speech-based systems for multifaceted mental health assessment in the general population, not only for detecting clinical thresholds but also for estimating their severity. Addressing fairness and incorporating uncertainty estimation with selective classification are key contributions that can enhance the clinical utility and responsible implementation of such systems.
Authors
- Xuan Cao (ORCID: https://orcid.org/0000-0001-7241-7188)
- Alexis Bourla (ORCID: https://orcid.org/0000-0003-1438-5733)
- Stéphane Mouchabac (ORCID: https://orcid.org/0000-0002-4226-7956)
- Rachid Riad (ORCID: https://orcid.org/0000-0002-7753-1219)
- Vincent Oustric
- Marc de Gennes (ORCID: https://orcid.org/0000-0003-1631-3231)
- A. Lesage
- M. Denais
Institutions
- Sorbonne Université (FR)
- Assistance Publique – Hôpitaux de Paris (FR)
- Hôpital Saint-Antoine (FR)
- Institut de Psychiatrie et Neurosciences de Paris (FR)
- Centre de Recherche Saint-Antoine (FR)
- Calliope Film Resources (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-09-26
- DOI
- https://doi.org/10.2196/58572
- Citations
- 15
- Primary Topic
- Emotion and Mood Recognition
- Type
- article
- Field-Weighted Citation Impact
- 7.61